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几种常见的sar图像去噪方法

于 2018-12-14 发布 文件大小:1KB
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  常见的几种去噪方法,维纳滤波器,均值滤波各种滤波(Extension of Phase Correlation to Subpixel Registration %Implementation of: % Extension of Phase Correlation to Subpixel Registration)

文件列表:

sar\frost.m, 969 , 2006-11-14
sar\lee.m, 2234 , 2006-11-14
sar\kaerman.m, 2085 , 2006-11-14
sar, 0 , 2007-03-01
www.pudn.com.txt, 218 , 2007-06-04

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    分割视图 希望大家多多批评指正 多多交流(segmentation View I hope Members can interact more criticism correction)
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    下面给出了48项泽尼克多项式,外加一项常数项。需要注意的是,读者并不需要严格按照下文所示的顺序排列这些泽尼克项,实际上在不同的应用和机构会采用不同的排列顺序。 表中的#0项是个常数或者说是平移项(piston term),这一项的系数也代表了平均光程差;而#1和#2项分别是x和y方向的倾斜项(tilt terms),#3代表了聚焦,因此,#1到#3项代表了波前的高斯或者近轴特性;#4和#5项代表了像散和离焦,#6和#7项代表彗差和倾斜,而#8项代表了3级像差和离焦,也就是说#4到#8项为3级相差项;同样地,#9到#15项代表了5级像差,而#16到#24项代表了7级像差,#25到#35项代表了9级像差,#36到#48项代表了11级像差。(Here are 48 Zernike polynomials, plus a constant term. It is important to note that the reader does not need to strictly arrange the Zernike entries in the order shown below, but in fact different orders of arrangement are applied to different applications and institutions. Table #0 is a constant or a shift (piston term), a coefficient of this term also represents the average optical path difference; while #1 and #2 are inclined X and Y direction (tilt terms), #3 represents the focus, therefore, #1 to #3 on behalf of the wavefront. Gauss or paraxial properties; #4 and #5 represent the astigmatism and defocus, #6 and #7 represent coma and tilt, and #8 represents the 3 order aberration and defocus, that is to say #4 to #8 is 3 level difference; similarly, #9 to #15 on behalf of the 5 order aberration #16 and #24, to represent the 7 level #25 to #35 aberration, on behalf of the 9 #36 to #48 aberration, on behalf of the 11 order aberration.)
    2017-10-09 16:29:16下载
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  • qoulen_v65
    基于多相结构的信道化接收机,sar图像去噪的几种新的方法,小波包分析提取振动信号中的特征频率。( Channelized receiver based on multi-phase structure, Several new methods sar image denoising, Wavelet packet analysis to extract vibration signal characteristic frequency.)
    2016-05-26 17:17:50下载
    积分:1
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    一个用于电子显微镜下运动细胞的识别跟踪的源代码,出自哈佛医学院的一位教授,对于做物体运动识别估计的各位很有帮助!(Under an electron microscope for cell identification and tracking movement of the source code, from a professor at Harvard Medical School, for the movement of objects do you identify the estimated useful!)
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    1.从图像中识别几何形状的基本方法;基本原理是在于利用点与线的对偶性,将原始图像空间给定的曲线通过曲线表达形式变为参数空间的一个点,这样就把原始图像中给定的检测问题转化为寻找参数空间的峰值问题。 2.基于Gabor变换的人眼检测MATLAB实现。 3.基于Radon的车牌矫正方法。 4.基于模型函数headata产生不同大小的头模型数据。 5.基于函数wdcbm2()设置图像分层阈值压缩参数,实现图像压缩(The basic method of identifying geometry from the image; the basic principle is to use point and line duality, the original image space curve given expression through the curve into a parameter space of a point, so the peak detection problem is given in the original image is transformed into searching the parameter space.)
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